精英个人协作搜索的 sinus cosine 算法及其在机械优化设计中的应用
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
一个新的Sine Cosine算法为精英个人协作搜索提高了优化准确性和融合速度. 这种增强的算法克服了传统方法的局限性,在模拟和机械设计实验中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 传统的Sine Cosine算法 (SCA) 的搜索准确度低,收速度慢,局部优化过早.
- 现有的智能优化算法往往难以有效地平衡全球勘探和本地开发.
- 需要改进的优化技术,适用于复杂的工程设计问题.
研究的目的:
- 为精英个人协作搜索 (SCA-EICS) 提出一个增强的正弦正弦算法,以解决SCA的局限性.
- 为了改善人口分布,全球勘探和当地剥削平衡.
- 为了提高收的准确性和速度,同时避免局部最佳.
主要方法:
- 使用帐混乱映射进行人口初始化,以改善分布.
- 使用超模触角函数对SCA参数进行非线性调整.
- 混合搜索策略结合了SCA,m-neighborhood本地搜索和全球最佳搜索,交替执行.
- 贪的选择策略来加速融合.
主要成果:
- 与标准SCA,其他改进的SCA变体,基于混乱的算法和其他智能优化器相比,SCA-EICS展示了优越的优化性能.
- 拟议的算法实现了更好的收精度和更快的收速度.
- 通过模拟和两个机械优化设计实验验验证的有效性.
结论:
- 精英个人协作搜索的Sine Cosine算法有效地克服了标准SCA的缺陷.
- 混合搜索策略和参数调整显著提高了优化能力.
- SCA-EICS显示出强大的可行性和适用于现实世界的工程优化任务.
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